Forecasting officer ‘aggression’ technique can be template for performance and training

Data crunching on an industrial scale is being tested as a new technique to help predict if a police officer is likely to misbehave on duty.

A pair of US research teams are using ‘big data’ processing to see if officers are in danger of reacting badly to individual members of the public – specifically in terms of “aggression” and “improper conduct”.

The two University of Chicago teams are in the early stages of working with the Chicago Police Department (CPD) and Charlotte-Mecklenburg Police Department in North Carolina to build a programme to improve their specialist Early Intervention Systems.

While data crunching has been used in policing since the late 1970s, applying this level of big-data processing — similar to techniques that help determine email spam, a person`s movie preferences or advertisements on a social media page — to determine police misconduct is new, experts say.

And they believe the principle can be widened to apply to other aspects of policing, in the fields of training and performance management.

However, the data scientists are reportedly encountering “deep suspicion” from officers concerned about the system`s fairness and effectiveness. The new approach also raises the complex issue of what to do once the system predicts an officer is likely to transgress.

The efforts come at a volatile time in Chicago and around the US generally.

CPD is facing a federal inquiry after last year`s release of video showing an officer fatally shooting Laquan McDonald 16 times in October 2014.

The release of another video earlier this month, from the scene of a July stolen car crash in which police fatally shot 18-year-old Paul O`Neal in the back, further agitated relations between the community and its police force.

Those incidents were followed by weekend rioting in Milwaukee after a police officer shot and killed a man who reportedly refused to drop his gun during a foot chase.

While the police misconduct application is one of the more controversial elements of this version of big-data processing, the researchers say their goal is broader.

“The thing we`re finding is that using it (big data) to predict officer adverse incidents is just one use,” said Rayid Ghani, director of the university’s Center for Data Science & Public Policy and previously chief data scientist for President Barack Obama`s 2012 campaign.

“Inside police departments, they are doing a lot of other things — performance management, other safety things, training. This is easily extensible to all those things.”

Jens Ludwig, director of the university’s Crime Lab, added: “Ultimately the goal here is that you want to train and retain the very highest-quality police force that you can.”

Most departments, including Charlotte-Mecklenburg, use a threshold system to determine if an officer is likely to have an “adverse interaction” with a member of the public and needs intervention.

That system typically flags up whether an officer has been involved in multiple “worrying” incidents — public complaints, vehicle accidents, on-the-job chases and injuries, or uses of excessive force — in a short time period.

The problem with threshold systems is that they place an inordinately high and inaccurate number of officers in the “at-risk” categories, while letting other officers in need of intervention slip by undetected, experts say.

The advantage of data-driven analysis is that it can take mounds of law enforcement information and look for patterns that lead to misconduct and those that lead to exemplary performance, supporters say.

Chicago police in 1994 became one of the first departments in the US to start a pilot Early Intervention System using data analysis. The software programme, called BrainMaker, was started partly in response to police union criticism that the existing human supervisors were too arbitrary and subjective.

It was abandoned less than two years later amid Fraternal Order of Police contentions that the system was too intrusive, unable to accurately assess the nuance of police w